Currently, the main efforts against visual forgeries focus on fake images from GAN.
Wang et al  [1]} find that the simple CNN model can easily detect fake images from open-world GAN.
Therefore, they argue that GAN-based fake images have common flaws, which allow us to tell them apart from real images.
Ning et al  [2]} find that we can track fake images' algorithm due to the fact that GAN-based fake images have their fingerprints.
This work further promotes accountability for fake images.
Girish et al  [3]} further propose a new attribution algorithm to deal with open-world challenges where the algorithms may be unknown to the detector image
To achieve this, Girish et al take advantage of out-of-distribution detection and clustering to define and incorporate the unseen algorithms.
